Density Functional Tight Binding (DFTB) is a semi-empirical quantum theory based on an expansion of the Kohn-Sham DFT total energy that holds promise as an alternate approach for gas-phase studies[1]} as well as condensed phase systems[2]}, [3]}. DFTB implements a balance of approximate quantum mechanics and empirical functions that can allow for several orders of magnitude improvement in computational efficiency for a variety of materials and mixtures[4]}. Difficulty can arise in creating new DFTB interaction potentials or improving upon existing models due to the large number of hyper-parameters within the approach. In particular, the approximate Hamiltonian in DFTB can be sensitive to the confining potential chosen for the wavefunction and electron density[5]} (Section REF ), and in general there does not exist a predefined recipe for how to choose these parameters nor how to explore that specific phase space. The Hamiltonian parameters in turn are closely coupled to the empirical repulsive energy, which itself has a wide variety of options in terms of functional form and data to be fit[6]}. In addition, the repulsive energy is usually taken to be strictly pairwise (two-center), though a number of systems can require many-body terms as well for accurate predictions[7]}. blackThe generation of training data can be problematic for high-Z or magnetic materials,[8]} where sufficient DFT-MD data can be too computationally demanding to compute and being able to leverage small MD training sets would be advantageous. In addition, machine learning approaches tend to be highly data intensive[9]}, which can pose difficulties for any method that leverages these techniques. Thus, DFTB method development would be holistically improved through a more automatic method for parameterization, where candidate models could be screened rapidly and efficiently, thereby allowing the user to quickly determine an optimal model for their specific needs.
